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Summarization: European Union has introduced the European Trading System (ETS) as a tool for developing and implementing international treaties related to climate changes and to identify the most cost-effective methods for reducing greenhouse gas emissions, in particular carbon dioxide (CO2), which is the most substantial. Companies producing carbon emissions must effectively manage associated costs by buying or selling carbon emission futures. Viewed from this perspective, this paper provides a model for managing the risk by buying and selling carbon emission futures by implementing techniques that leverage computational intelligence. Three computational intelligence techniques are proposed to provide accurate and timely forecasts for changes in the price of carbon: A novel hybrid neuro-fuzzy controller that forms a closed-loop feedback mechanism called PATSOS; an artificial neural network (ANN) based system; an adaptive neuro-fuzzy inference system (ANFIS). Results are based on 1074 daily carbon price observations collected to comprise a useful time-series dataset and for evaluation of the proposed techniques. The extra-sample performance of the proposed techniques is calculated. Analysis results are compared with those produced by other models. Comparison studies reveal that PATSOS is the most accurate and promising methodology for predicting the price of carbon. It is stated that this paper registers a first attempt to apply a hybrid neuro-fuzzy controller to forecasting carbon prices. ; Presented on: Applied Soft Computing

Summarization: Many researchers have tried to model the human behavior in stock markets. Because stock markets are influenced by many economical, political and psychological factors, it is very difficult to forecast the future stock prices. However, it is impossible to develop a precise mathematical model that can predict crisp numerical values of the stock prices. This is due to uncertainties involved in these parameters, the uncertain behavior of the factors, and tolerances. The potential of fuzzy logic in developing a model for characterization by approximate reasoning really lies here. This paper presents a modest attempt to catch rather the turning points of stock prices by using the Fuzzy Inference System (FIS) rather than the absolute prices. The methodology consists of two steps: (1) developing the basic model using special data to train the model and (2) validating the basic model by using the special and raw stock exchange data. The results have indicated that the fuzzy inference system provides a prudent way to capture uncertainty (non-statistical) in relationships among parameters that control the turning points of the stock prices. The novelty of this article is the selected data used for training the model. Each sample of data includes four values that correspond to the stock prices of four continued sessions. These data samples are then divided into the training and checking data. In the evaluation phase, unseen data are presented in the model in order to forecast the turning point of the stock. ; Παρουσιάστηκε στο: International Journal of Engineering and Management

Summarization: Natural disasters have caused over a million of deaths and $3 trillion in economic losses during the last 20 years. However, theoretical and empirical studies have not reached a conclusion as for their effect on economic growth, and the results can best be described as mixed. The present study proposes the use of a quantile on quantile (QQ) approach to shed more light on this complex relationship. This approach combines the standard quantile regression analysis with nonparametric estimations and allows us to examine how different quantiles of natural disasters affect different quantiles of GDP growth. Using data from over 100 countries over a 30-years period, we confirm that the results of the QQ approach differ from the ones obtained by standard approaches like fixed effects regressions. We document that the relationship between the intensity of natural disasters and economic growth is mostly negative. Nonetheless, there are some exceptions to this. Our findings reveal that the effect of natural disasters can be occasionally positive, depending on the quantiles that we examine. The magnitude of the effect also differs across different combinations of the quantile of economic growth and the quantile of natural disasters. Finally, we obtain somewhat different results when we estimate separate QQ regressions for groups of countries that differ in terms of climate, economic and democratic development, and across different year lags of the natural disaster index. ; Presented on: Annals of Operations Research